Heart disease is the leading cause of death in the United States, and echocardiography (or "echo") is the most common way doctors look at the heart. Echo is safe, painless, and can detect major heart problems, including weak heart pumping and valve disease.
Valve disease, especially aortic stenosis (narrowing) and mitral regurgitation (leakage), is common in older adults but often goes undiagnosed. While echo is the main tool for finding valve problems, it takes time, requires expert training, and results can vary between readers.
Recent advances in artificial intelligence (AI), especially deep learning (DL), have shown promise in automatically analyzing heart images. However, past research hasn't fully tackled key echo techniques-like color Doppler and spectral Doppler-that are crucial for measuring how blood moves through heart valves. AI tools also face challenges in being used in everyday medical practice because of workflow issues, lack of real-world testing, and concerns about how the algorithms make decisions.
At Columbia University Irving Medical Center, researchers have built a large database of heart tests over the last six years and developed AI programs to analyze echocardiograms. The current study will test whether providing AI analysis to cardiologists in real time during echo reading can make the process faster and more consistent.
Attending cardiologist employed by Columbia University, ColumbiaDoctors, or NewYork Presbyterian Hospital who reads transthoracic echocardiograms in the Columbia echocardiography laboratory
Provided informed consent to take part in the questionnaires or pivotal study
Physician in training (cardiology fellow or advanced imaging fellow)
Board-certified attending cardiologists at Columbia University, ColumbiaDoctors, or NewYork-Presbyterian Hospital who interpret transthoracic echocardiograms in the Columbia echocardiography laboratory and have provided informed consent to participate
Case-control
Prospective
Not listed
Proportion of Clinically Meaningful Reclassification by Panel Review
Proportion of cases where the expert panel reclassifies valvular regurgitation severity by at least one grade (upgrade or downgrade). The proportion will be calculated as the number of cases with reclassification ÷ total number of cases reviewed.
Proportion of Cases with AI-Based Reclassification Leading to a Change in Clinical Management
The proportion will be calculated as the number of cases with any management change ÷ total number of cases reviewed.
Proportion of Cases with AI-Based Reclassification Leading to Referral to a Valve Specialist or Surgeon
Definition: The proportion will be calculated as the number of cases referred to a valve specialist or surgeon ÷ total number of cases reviewed.
Proportion of Cases with AI-Based Reclassification Leading to a Change in Frequency of Follow-Up Echocardiography
The proportion will be calculated as the number of cases with a change in recommended follow-up echocardiography frequency ÷ total number of cases reviewed.
Proportion of Cases with AI-Based Reclassification Leading to Referral for Further Testing (TEE or Cardiac MRI)
The proportion will be calculated as the number of cases referred for further testing with TEE or cardiac MRI ÷ total number of cases reviewed.
Concordance Between AI and Panel Review
Proportion of cases where AI classification agrees with expert panel review of valvular regurgitation severity.
Concordance Between Cardiologist Clinical Read and Panel Review
Proportion of cases where cardiologist clinical interpretation agrees with expert panel review of valvular regurgitation severity.
Comparison of Concordance Rates (AI vs Cardiologist) Against Panel Review
Difference between the concordance rate of AI vs panel review and the concordance rate of cardiologist clinical read vs panel review.
Inter-Reader Agreement for Categorical Echocardiographic Measures
Agreement between independent cardiologist readers for categorical variables (e.g., severity of valvular regurgitation) will be quantified using Cohen's kappa statistic.
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Columbia University
Lead sponsor
American Heart Association
Collaborator
